Segmentation of 3D Articulated Objects by Dynamic Grouping of Discontinuities

Segmentation of 3D articulated objects into a small number of parts is an important step for recognition of objects in 3D space. Range data provides ready-to-use information to derive a reasonably stable and accurate differential structure of the surfaces, which allow us to track discontinuities between these parts. We present a mixed and comprehensive approach to segmenting and extracting prototypical parts from 3D articulated objects. Discontinuity points detected from the images are combined using a "snake-type" minimization technique, and deformable superquadric models are fitted to the resulting regions afterwards. We compare our approach with others for part-based segmentation.